{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pca降维处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nPvWpokcBAIDndeaZZ+aWW27J5Zdfnt/85jd529veluOPPz4PP/xwkuSnP/1p9thjj1x99dXZfffdM23atLz//e/Pk08++aI+j7ADAABsUw0NDdlll12y22675UMf+lCOOeaY/OQnP0nyp0uk/f3f/3123XXX7L333kk2PXujUqnk0ksvzVvf+tY0NTVlzz337H+NjRYsWJA///M/z+jRozNq1KjMmDEjv//97wd8no1mzpyZM888M2eeeWbGjBmT5ubmfOYzn0m1Wu1f893vfjevec1rMmrUqOyyyy5517veleXLl2/1++7o6Mh73/venHDCCfnJT36SY445JrvvvnsOO+ywnH/++fn617/ev/aXv/xlXvva16ahoSGTJk3KJz7xiQFnNc2cOTMf/vCHc9ZZZ2WnnXbKxIkT82//9m9Zt25d3vve92bUqFGZPn16/t//+3/9z9l4qbif/exnedWrXpXGxsYcfvjhue+++wbMeeWVV2b//fdPQ0NDpk2blgsuuGDA/mnTpuUf/uEf8r73vS+jRo3K1KlT841vfGPAmsWLF+ftb397xo4dm3HjxuWkk07Ko48+2r9/49f//PPPz6RJk7LzzjvnjDPOSHd3d//7e+yxx/I3f/M3/Wd4bclZZ52VT3ziEzn88MO3+n8LAADY0RYtWpRvfetb+eEPf5gZM2bkFa94Rf72b/82r3vd6/Ktb30rSfKHP/whjz32WH74wx/mO9/5Ti677LLcddddOfnkk1/U5xJ2AACA7Wr48OH9Z+Ykyfz58/Pggw/m2muvzdVXX73F533uc5/L29/+9vzmN7/JCSeckHe/+939v8m2dOnSvP71r09DQ0Ouu+663HXXXXnf+973vJd8+/a3v526urrcfvvtmTt3bv7lX/4ll156af/+7u7unHfeebn33ntz1VVX5dFHH82pp5661e/zv//7v7Ny5cp87GMf2+z+jWcQLV26NCeccEIOPfTQ3Hvvvbn44ovz7//+7/nCF76wybzNzc25/fbb8+EPfzgf+tCH8ra3vS1HHnlkfv3rX+fYY4/N//2//zcdHR0Dnvd3f/d3ueCCC3LHHXdk/PjxefOb39wfVO666668/e1vzymnnJLf/va3+exnP5vPfOYzm1y27oILLshrXvOa3H333fnrv/7rfOhDH8qDDz7Y/3U67rjjMmrUqLS2tuamm27KyJEjc/zxxw/43/n666/P73//+1x//fX59re/ncsuu6z/88ybNy8tLS35/Oc/n7a2trS1tW311xkAAAaj3/72t+np6clee+2VkSNH9v/55S9/2f8LaL29vens7Mx3vvOdzJgxIzNnzsy///u/5/rrr+//fntr1G2vNwEAAAxt1Wo18+fPz3//93/nwx/+cP/2ESNG5NJLL019ff3zPv/UU0/NO9/5ziTJP/zDP+QrX/lKbr/99hx//PH52te+ljFjxuTyyy/PsGHDkiR77bXX877elClT8uUvfzmVSiV77713fvvb3+bLX/5yTj/99CTJ+973vv61e+yxR77yla/k0EMPzdq1azNy5MgXfL8bL6+wzz77PO+6f/3Xf82UKVNy0UUXpVKpZJ999snjjz+ej3/84znnnHNSU9P3+3cHHnhgPv3pTydJPvnJT+aLX/ximpub++c955xzcvHFF+c3v/nNgLNZzj333P5rdH/7299OS0tLfvSjH+Xtb397/uVf/iVvfOMb+y8Lt9dee+V3v/tdvvSlLw2IWCeccEL++q//Okny8Y9/PF/+8pdz/fXXZ++9984PfvCD9Pb25tJLL+0/0+Zb3/pWxo4dmxtuuCHHHntskmSnnXbKRRddlNra2uyzzz458cQTM3/+/Jx++ukZN25camtr+8+OAgCAslu7dm1qa2tz1113pba2dsC+jf+emDRpUurq6gb822XfffdN0nfGz8YrGrwQZ+wAAADb1NVXX52RI0emsbExf/Znf5Z3vOMd+exnP9u//4ADDnjBqJMkr3rVq/o/HjFiREaPHt1/abR77rknM2bM6I86W+Pwww8fcMmvI444Ig8//HB6enqS9J3N8uY3vzlTp07NqFGj8oY3vCFJ3z+wtsazL+v2fO6///4cccQRA2Y56qijsnbt2ixZsqR/27Pff21tbXbeeecccMAB/dsmTpyYJJtcLu6II47o/3jcuHHZe++9c//99/d/7qOOOmrA+qOOOmrA1+G5n7tSqWSXXXbp/zz33ntvFi5cmFGjRvX/FuK4ceOyfv36/t9ETJL9999/wD9oJ02a9KIubQcAAGVy8MEHp6enJ8uXL8/06dMH/Nn4y0xHHXVUNmzYMOD75oceeihJsttuu23153LGDgAAsE0dffTRufjii1NfX59dd901dXUD/9kxYsSIrXqd50abSqWS3t7eJH2Xd9uW1q1bl+OOOy7HHXdcvve972X8+PFZtGhRjjvuuAGXF3s+G3/r7oEHHhgQV16qzb3/Z2/bGIY2fk22pef72q9duzaHHHJIvve9723yvPHjx2/VawAAQBmtXbs2Cxcu7H/8yCOP5J577sm4ceOy11575d3vfnfe85735IILLsjBBx+cFStWZP78+XnVq16VE088Mcccc0xe/epX533ve18uvPDC9Pb25owzzsib3vSmF7wCwbM5YwcAANimRowYkenTp2fq1KmbRJ1t5VWvelVaW1v77x2zNW677bYBj2+99dbsueeeqa2tzQMPPJA//vGP+eIXv5gZM2Zkn332edFnlxx77LFpbm7OP//zP292f3t7e5K+Sy3ccsstA87wuemmmzJq1Ki0tLS8qM+5Obfeemv/x0899VQeeuih/ss77LvvvrnpppsGrL/pppuy1157bXK5iC159atfnYcffjgTJkzY5DcRx4wZs9Vz1tfXDzhLCAAABrs777wzBx98cA4++OAkydlnn52DDz4455xzTpK+SxS/5z3vyUc/+tHsvffemTVrVu64445MnTo1SVJTU5Of/vSnaW5uzutf//qceOKJ2XfffXP55Ze/qDmEHQAAoHTOPPPMrF69OqecckruvPPOPPzww/nud7/7vDccXbRoUc4+++w8+OCD+c///M989atfzZw5c5IkU6dOTX19fb761a/mD3/4Q37yk5/kvPPOe1Ezbbx30M9+9rO85S1vyS9+8Ys8+uijufPOO/Oxj30sH/zgB5Mkf/3Xf53Fixfnwx/+cB544IH8+Mc/zrnnnpuzzz67//46/xuf//znM3/+/Nx333059dRT09zcnFmzZiVJPvrRj2b+/Pk577zz8tBDD+Xb3/52Lrroovzt3/7tVr/+u9/97jQ3N+ekk05Ka2trHnnkkdxwww35yEc+MuBSci9k2rRpufHGG7N06dKsXLlyi+uWLVuWe+65p/83I3/729/mnnvuyZNPPrnVnwsAALaFmTNnplqtbvLnsssuS9J31vrnPve5PPLII+nq6srjjz+eefPmDbik8q677porr7wya9asybJly/Ktb30r48aNe1FzCDsAAEDp7Lzzzrnuuuuydu3avOENb8ghhxySf/u3f3vee+685z3vydNPP53Xvva1OeOMMzJnzpx84AMfSNJ3CbHLLrssP/zhD7Pffvvli1/8Ys4///wXPddJJ52Um2++OcOGDcu73vWu7LPPPnnnO9+ZVatW5Qtf+EKSZPLkybnmmmty++2358ADD8wHP/jBnHbaafn0pz/90r4Yz/HFL34xc+bMySGHHJJly5blpz/9af89jV796lfnv/7rv3L55Zfnla98Zc4555x8/vOfz6mnnrrVr9/U1JQbb7wxU6dOzezZs7PvvvvmtNNOy/r16zN69Oitfp3Pf/7zefTRR/OKV7xiwCXcnuuSSy7JwQcfnNNPPz1J8vrXvz4HH3xwfvKTn2z15wIAgJeTSnVr7/AJAABQUjNnzsxBBx2UCy+8sOhRtpsbbrghRx99dJ566qmMHTu26HEAAIDtxBk7AAAAAAAAJSHsAAAAAAAAlIRLsQEAAAAAAJSEM3YAAAAAAABKQtgBAAAAAAAoCWEHAAAAAACgJIQdAAAAAACAkhB2AAAAAAAASkLYAQAAAAAAKAlhBwAAAAAAoCSEHQAAAAAAgJL4/wAcFrRnPu/MGAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 2000x3000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.decomposition import PCA\n",
    "import matplotlib.pyplot as plt\n",
    "import csv\n",
    "# 假设有三个数据集，每个数据集都是一个矩阵，行数相同\n",
    "def to_ndarray(input_file):\n",
    "    with open(input_file, 'r', newline='') as csvfile:\n",
    "         reader = csv.reader(csvfile)\n",
    "         data = list(reader) \n",
    "    for row in data:\n",
    "        del row[14]\n",
    "        del row[12]\n",
    "        del row[5]\n",
    "        del row[5]\n",
    "       # row=[cell.replace('\"', '') for cell in row]\n",
    "        row=[cell.replace('\\\\n', '') for cell in row]\n",
    "    sum=0\n",
    "    \n",
    "    data_array = np.array(data, dtype=float)\n",
    "    return data_array\n",
    "\n",
    "# 生成三个随机数据集\n",
    "au=to_ndarray('output_au.csv')\n",
    "ca=to_ndarray('output_ca.csv')\n",
    "inn=to_ndarray('output_in.csv')\n",
    "uk=to_ndarray('output_uk.csv')\n",
    "us=to_ndarray('output_us.csv')\n",
    "s_au=au.shape[0]\n",
    "s_ca=ca.shape[0]\n",
    "s_inn=inn.shape[0]\n",
    "s_uk=uk.shape[0]\n",
    "s_us=us.shape[0]\n",
    "\n",
    "\n",
    "# 合并三个数据集为一个大的数据矩阵\n",
    "all_data = np.vstack([au,ca,inn,uk,us])\n",
    "\n",
    "# 创建PCA对象，指定保留的主成分数量为2\n",
    "pca = PCA(n_components=10)\n",
    "\n",
    "# 在所有数据上进行PCA\n",
    "principal_components = pca.fit_transform(all_data)\n",
    "\n",
    "# 提取每个数据集对应的主成分数据\n",
    "pc_data1 = principal_components[:s_au]\n",
    "pc_data2 = principal_components[s_au:s_ca]\n",
    "pc_data3 = principal_components[s_ca:s_inn]\n",
    "pc_data4=principal_components[s_inn:s_uk]\n",
    "pc_data5=principal_components[s_uk:]\n",
    "\n",
    "# 可视化主成分分析结果\n",
    "plt.figure(figsize=(20, 30))\n",
    "plt.scatter(pc_data1[:1000, 0], pc_data1[:1000, 1], color='b', label='au', alpha=1)\n",
    "plt.scatter(pc_data2[:1000, 0], pc_data2[:1000, 1], color='g', label='ca', alpha=1)\n",
    "plt.scatter(pc_data3[:1000, 0], pc_data3[:1000, 1], color='r', label='inn', alpha=1)\n",
    "plt.scatter(pc_data4[:1000, 0], pc_data4[:1000, 1], color='c', label='uk', alpha=1)\n",
    "plt.scatter(pc_data5[:1000, 0], pc_data5[:1000, 1], color='y', label='us', alpha=0.5)\n",
    "plt.title('PCA of five Datasets')\n",
    "plt.xlabel('Principal Component 1')\n",
    "plt.ylabel('Principal Component 2')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "有效因子分析与碎石图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               0             1            2           3           4\n",
      "0       0.029400      0.023741     0.008918    0.116116   -0.074469\n",
      "1      -0.005064      0.047394     0.030879    0.435426    0.116667\n",
      "2      -0.004338      0.042364     0.018404    0.082111   -0.070966\n",
      "3       0.024788      0.050115    -0.020343    0.343315    0.016050\n",
      "4       0.003425     -0.019681    -0.032211   -0.143038    0.175270\n",
      "5      -7.126274      2.556204    -1.203336  529.102552  -31.169275\n",
      "6      -0.986977      0.149345    -0.989974  136.859183  120.477082\n",
      "7     428.466974  17348.344060  -781.988041   -0.089843    0.002133\n",
      "8     458.375326   1981.529679  6839.540674    0.103491    0.012170\n",
      "9   41691.974222   -200.072522   -67.159666    0.093417   -0.002630\n",
      "10     -0.157327      0.045093     0.009280   -0.460169    0.091128\n",
      "11      0.002218     -0.005170     0.000204    0.008629    0.038527\n",
      "12      0.002235     -0.005205     0.000214    0.008622    0.038713\n",
      "13      0.002199     -0.005133     0.000192    0.008637    0.038339\n",
      "14      0.000577     -0.001314     0.000172    0.001184    0.008884\n",
      "15      0.000684     -0.001525     0.000167    0.002631    0.010043\n",
      "16      0.000079     -0.000385    -0.000113    0.001350    0.003876\n",
      "17     -0.000076      0.000157    -0.000131    0.000814   -0.000235\n",
      "18     -0.000037     -0.000045    -0.000058   -0.000065    0.001029\n",
      "19     -0.000061     -0.000241    -0.000178    0.001116    0.003346\n",
      "20      0.001048     -0.001814     0.000344    0.001594    0.011567\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.stats import pearsonr\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "import math\n",
    "from sklearn.decomposition import FactorAnalysis\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "def clean_and_convert(x):\n",
    "    if isinstance(x, str):\n",
    "        x = x.replace('\"', '').replace(\"'\", '')  # 去除单引号和双引号\n",
    "        return float(x)  # 转换为浮点数\n",
    "    else:\n",
    "        return float(x)  # 如果是整数，直接转换为浮点数\n",
    "def to_pd(input):\n",
    "    df = pd.read_csv(input)\n",
    "    #一下数据与本次问题无关\n",
    "    df=df.drop(df.columns[14],axis=1)\n",
    "    df=df.drop(df.columns[12],axis=1)\n",
    "    df=df.drop(df.columns[5],axis=1)\n",
    "    df=df.drop(df.columns[5],axis=1)  \n",
    "    df=df.applymap(clean_and_convert)\n",
    "    return df\n",
    "\n",
    "df1=to_pd('output_au.csv')\n",
    "df2=to_pd('output_ca.csv')\n",
    "df3=to_pd('output_uk.csv')\n",
    "df4=to_pd('output_us.csv')\n",
    "df5=to_pd('output_in.csv')\n",
    "\n",
    "\n",
    "\n",
    "# 进行因子分析\n",
    "fa = FactorAnalysis(n_components=5)\n",
    "df=np.vstack([df1,df2,df3,df4,df5])\n",
    "fa.fit(df)\n",
    "df=pd.DataFrame(df)\n",
    "# 查看因子负荷矩阵\n",
    "loadings = pd.DataFrame(fa.components_.T, index=df.columns)\n",
    "print(loadings)\n",
    "\n",
    "# 绘制碎石图（Scree Plot）\n",
    "evr = fa.noise_variance_\n",
    "plt.plot(np.arange(1, len(evr) + 1), evr, marker='o', linestyle='-', color='b')\n",
    "plt.xlabel('Number of Components')\n",
    "plt.ylabel('Explained Variance Ratio')\n",
    "plt.xticks(np.arange(1, len(evr) + 1))\n",
    "plt.title('global reliable')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一致性检验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1015341, 110)\n",
      "(1015341, 50)\n",
      "Cronbach's α: (0.6943535047934039, array([0.694, 0.695]))\n"
     ]
    }
   ],
   "source": [
    "from pingouin import reliability\n",
    "import pandas as pd\n",
    "dff=pd.read_csv('d.csv')\n",
    "\n",
    "print(dff.shape)\n",
    "# 计算 Cronbach's α\n",
    "temp=dff.iloc[:, :50]\n",
    "print(temp.shape)\n",
    "alpha = reliability.cronbach_alpha(temp)\n",
    "\n",
    "print(f\"Cronbach's α: {alpha}\")\n"
   ]
  }
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